μCB-seq: microfluidic cell barcoding and sequencing for high-resolution imaging and sequencing of single cells

Opto-combinatorial indexing enables high-content transcriptomics by linking cell images and transcriptomes

We introduce a simple integrated analysis method that links cellular phenotypic behaviour with single-cell RNA sequencing (scRNA-seq) by utilizing a combination of optical indices from cells and hydrogel beads. With our method, the combinations, referred to as joint colour codes, enable the link via matching the optical combinations measured by conventional epi-fluorescence microscopy with the concatenated DNA molecular barcodes created by cell-hydrogel bead pairs and sequenced by next-generation sequencing. We validated our approach by demonstrating an accurate link between the cell image and scRNA-seq with mixed species experiments, longitudinal cell tagging by electroporation and lipofection, and gene expression analysis. Furthermore, we extended our approach to multiplexed chemical transcriptomics, which enabled us to identify distinct phenotypic behaviours in HeLa cells treated with various concentrations of paclitaxel, and determine the corresponding gene regulation associated with the formation of a multipolar spindle.

Single-cell RNA sequencing in osteoarthritis

Osteoarthritis is a progressive and heterogeneous joint disease with complex pathogenesis. The various phenotypes associated with each patient suggest that better subgrouping of tissues associated with genotypes in different phases of osteoarthritis may provide new insights into the onset and progression of the disease. Recently, single-cell RNA sequencing was used to describe osteoarthritis pathogenesis on a high-resolution view surpassing traditional technologies. Herein, this review summarizes the microstructural changes in articular cartilage, meniscus, synovium and subchondral bone that are mainly due to crosstalk amongst chondrocytes, osteoblasts, fibroblasts and endothelial cells during osteoarthritis progression. Next, we focus on the promising targets discovered by single-cell RNA sequencing and its potential applications in target drugs and tissue engineering. Additionally, the limited amount of research on the evaluation of bone-related biomaterials is reviewed. Based on the pre-clinical findings, we elaborate on the potential clinical values of single-cell RNA sequencing for the therapeutic strategies of osteoarthritis. Finally, a perspective on the future development of patient-centred medicine for osteoarthritis therapy combining other single-cell multi-omics technologies is discussed. This review will provide new insights into osteoarthritis pathogenesis on a cellular level and the field of applications of single-cell RNA sequencing in personalized therapeutics for osteoarthritis in the future.

nuPRISM: Microfluidic Genome-Wide Phenotypic Screening Platform for Cellular Nuclei

Genome-wide loss-of-function screens are critical tools to identify novel genetic regulators of intracellular proteins. However, studying the changes in the organelle-specific expression profile of intracellular proteins can be challenging due to protein localization differences across the whole cell, hindering context-dependent protein expression and activity analyses. Here, we describe nuPRISM, a microfluidics chip specifically designed for large-scale isolated nuclei sorting. The new device enables rapid genome-wide loss-of-function phenotypic CRISPR-Cas9 screens directed at intranuclear targets. We deployed this technology to identify novel genetic regulators of β-catenin nuclear accumulation, a phenotypic hallmark of APC-mutated colorectal cancer. nuPRISM expands our ability to capture aberrant nuclear morphological and functional traits associated with distinctive signal transduction and subcellular localization-driven functional processes with substantial resolution and high throughput.

Frontiers in single cell analysis: multimodal technologies and their clinical perspectives

Single cell multimodal analysis is at the frontier of single cell research: it defines the roles and functions of distinct cell types through simultaneous analysis to provide unprecedented insight into cellular processes. Current single cell approaches are rapidly moving toward multimodal characterizations. It replaces one-dimensional single cell analysis, for example by allowing for simultaneous measurement of transcription and post-transcriptional regulation, epigenetic modifications and/or surface protein expression. By providing deeper insights into single cell processes, multimodal single cell analyses paves the way to new understandings in various cellular processes such as cell fate decisions, physiological heterogeneity or genotype-phenotype linkages. At the forefront of this, microfluidics is key for high-throughput single cell analysis. Here, we present an overview of the recent multimodal microfluidic platforms having a potential in biomedical research, with a specific focus on their potential clinical applications.

Microfluidic Compartmentalization Platforms for Single Cell Analysis

Many cellular analytical technologies measure only the average response from a cell population with an assumption that a clonal population is homogenous. The ensemble measurement often masks the difference among individual cells that can lead to misinterpretation. The advent of microfluidic technology has revolutionized single-cell analysis through precise manipulation of liquid and compartmentalizing single cells in small volumes (pico- to nano-liter). Due to its advantages from miniaturization, microfluidic systems offer an array of capabilities to study genomics, transcriptomics, and proteomics of a large number of individual cells. In this regard, microfluidic systems have emerged as a powerful technology to uncover cellular heterogeneity and expand the depth and breadth of single-cell analysis. This review will focus on recent developments of three microfluidic compartmentalization platforms (microvalve, microwell, and microdroplets) that target single-cell analysis spanning from proteomics to genomics. We also compare and contrast these three microfluidic platforms and discuss their respective advantages and disadvantages in single-cell analysis.

Single cell biology-a Keystone Symposia report

Single cell biology has the potential to elucidate many critical biological processes and diseases, from development and regeneration to cancer. Single cell analyses are uncovering the molecular diversity of cells, revealing a clearer picture of the variation among and between different cell types. New techniques are beginning to unravel how differences in cell state-transcriptional, epigenetic, and other characteristics-can lead to different cell fates among genetically identical cells, which underlies complex processes such as embryonic development, drug resistance, response to injury, and cellular reprogramming. Single cell technologies also pose significant challenges relating to processing and analyzing vast amounts of data collected. To realize the potential of single cell technologies, new computational approaches are needed. On March 17-19, 2021, experts in single cell biology met virtually for the Keystone eSymposium "Single Cell Biology" to discuss advances both in single cell applications and technologies.

Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision

RNA-seq, including single cell RNA-seq (scRNA-seq), is plagued by insufficient sensitivity and lack of precision. As a result, the full potential of (sc)RNA-seq is limited. Major factors in this respect are the presence of global bias in most datasets, which affects detection and quantitation of RNA in a length-dependent fashion. In particular, scRNA-seq is affected by technical noise and a high rate of dropouts, where the vast majority of original transcripts is not converted into sequencing reads. We discuss these biases origins and implications, bioinformatics approaches to correct for them, and how biases can be exploited to infer characteristics of the sample preparation process, which in turn can be used to improve library preparation.

Paper-thin multilayer microfluidic devices with integrated valves

Integrated valve microfluidics has an unparalleled capability to automate rapid delivery of fluids at the nanoliter scale for high-throughput biological experimentation. However, multilayer soft lithography, which is used to fabricate valve-microfluidics, produces devices with a minimum thickness of around five millimeters. This form-factor limitation prevents the use of such devices in experiments with limited sample thickness tolerance such as 4-pi microscopy, stimulated Raman scattering microscopy, and many forms of optical or magnetic tweezer applications. We present a new generation of integrated valve microfluidic devices that are less than 300 μm thick, including the cover-glass substrate, that resolves the thickness limitation. This "thin-chip" was fabricated through a novel soft-lithography technique that produces on-chip micro-valves with the same functionality and reliability of traditional thick valve-microfluidic devices despite the orders of magnitude reduction in thickness. We demonstrated the advantage of using our thin-chip over traditional thick devices to automate fluid control while imaging on a high-resolution inverted microscope. First, we demonstrate that the thin-chip provides an improved signal to noise when imaging single cells with two-color stimulated Raman scattering (SRS). We then demonstrated how the thin-chip can be used to simultaneously perform on-chip magnetic manipulation of beads and fluorescent imaging. This study reveals the potential of our thin-chip in high-resolution imaging, sorting, and bead capture-based single-cell multi-omics applications.

Microfluidic sample preparation for respiratory virus detection: A review

Techniques used to prepare clinical samples have been perfected for use in diagnostic testing in a variety of clinical situations, e.g., to extract, concentrate, and purify respiratory virus particles. These techniques offer a high level of purity and concentration of target samples but require significant equipment and highly trained personnel to conduct, which is difficult to achieve in resource-limited environments where rapid testing and diagnostics are crucial for proper handling of respiratory viruses. Microfluidics has popularly been utilized toward rapid virus detection in resource-limited environments, where most devices focused on detection rather than sample preparation. Initial microfluidic prototypes have been hindered by their reliance on several off-chip preprocessing steps and external laboratory equipment. Recently, sample preparation methods have also been incorporated into microfluidics to conduct the virus detection in an all-in-one, automated manner. Extraction, concentration, and purification of viruses have been demonstrated in smaller volumes of samples and reagents, with no need for specialized training or complex machinery. Recent devices show the ability to function independently and efficiently to provide rapid, automated sample preparation as well as the detection of viral samples with high efficiency. In this review, methods of microfluidic sample preparation for the isolation and purification of viral samples are discussed, limitations of current systems are summarized, and potential advances are identified.

μDamID: A Microfluidic Approach for Joint Imaging and Sequencing of Protein-DNA Interactions in Single Cells

DNA adenine methyltransferase identification (DamID) measures a protein's DNA-binding history by methylating adenine bases near each protein-DNA interaction site and then selectively amplifying and sequencing these methylated regions. Additionally, these interactions can be visualized using m6A-Tracer, a fluorescent protein that binds to methyladenines. Here, we combine these imaging and sequencing technologies in an integrated microfluidic platform (μDamID) that enables single-cell isolation, imaging, and sorting, followed by DamID. We use μDamID and an improved m6A-Tracer protein to generate paired imaging and sequencing data from individual human cells. We validate interactions between Lamin-B1 protein and lamina-associated domains (LADs), observe variable 3D chromatin organization and broad gene regulation patterns, and jointly measure single-cell heterogeneity in Dam expression and background methylation. μDamID provides the unique ability to compare paired imaging and sequencing data for each cell and between cells, enabling the joint analysis of the nuclear localization, sequence identity, and variability of protein-DNA interactions. A record of this paper's transparent peer review process is included in the Supplemental Information.